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Physics Colloquium - Thursday, February
Center; Refreshments at 3:30 P.M. in
Sloppy Modeling of Biochemical Networks and Human Genetic History
Theoretical Biology and Biophysics & Center for Nonlinear Studies
Los Alamos National Laboratory
Nonlinear models with large numbers of difficult-to-measure parameters
appear commonly in many fields of science, particularly in biology.
Here I focus on modeling the dynamics of biochemical reaction networks
and the spread of genetic variation among human populations. In both
cases, estimating parameters and uncertainties is a major obstacle to
developing useful models. I demonstrate that nonlinear models,
particular of biochemical networks, exhibit a universal "sloppy"
pattern of sensitivity to parameter variation; different directions in
parameter space vary by orders of magnitude in their constraint.
Consequently, predictions may be usefully constrained even when the
available data only very poorly constrain individual parameter values.
I also present a powerful method for inferring models of demographic
history from population genetic data. With this method, we have
developed the most complex statistically well-characterized model of
human genetic history to date.